AI-BASED DECISION SUPPORT MODELING IN DIGITAL MARKETING USING SEM
DOI:
https://doi.org/10.61837/mbuir040126118mKeywords:
artificial intelligence, decision support systems, digital marketing, user engagement, Structural Equation Modeling (SEM)Abstract
Contemporary digital marketing is increasingly shaped by the application of AI-based systems and intelligent chatbot solutions, which generate extensive data on user interactions and behavioral patterns. Although the existing literature thoroughly examines the relationships between these technologies, user engagement, and behavioral outcomes, limited attention has been given to the development of formalized models that integrate these relationships into managerial decision-making support. This paper proposes an analytical framework based on Structural Equation Modeling (SEM), in which user engagement and behavioral outcomes are analyzed through a formal structural model, while AI-based systems and chatbot systems are considered at a conceptual level as potential influencing factors. The objective of the study is to demonstrate how the identified structural relationships can serve as a quantitative foundation for analyzing relationships in digital marketing. The results indicate that user engagement plays a key role as a mediating mechanism between digital interactions and behavioral outcomes. The proposed framework provides a foundation for further empirical research and the extension of the model in real business environments.
References
Dwivedi, Y. K., et al. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research. International Journal of Information Management, 57, 102269. https://doi.org/10.1016/j.ijinfomgt.2020. 102269https://doi.org/10.1016/j.ijinfo mgt.2020.102269
Gnewuch, U., Morana, S., & Maedche, A. (2017). Towards designing cooperative and social conver sational agents for customer service. In Proceedings of the International Conference on Information Systems (ICIS), Seoul, South Korea.
Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Customer brand engagement in social media: Conceptualization, scale development and valida tion. Journal of Interactive Marketing, 28(2), 149–165. https://doi.org/10.1016/j.intmar.2013.12.002https:// doi.org/10.1016/j.intmar.2013.12.002
Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring customer rela tionships beyond purchase. Journal of Marketing Theory and Practice, 20(2), 122–146. https://doi. org/10.2753/MTP1069-6679200201
Brodie, R. J., et al. (2011). Customer engage ment: Conceptual domain, fundamental prop ositions, and implications for research. Journal of Service Research, 14(3), 252–271. https://doi. org/10.1177/1094670511411703
Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring customer rela tionships beyond purchase. Journal of Marketing Theory and Practice, 20(2), 122–146. https://doi. org/10.2753/MTP1069-6679200201
Brodie, R. J., et al. (2011). Customer engage ment: Conceptual domain, fundamental prop ositions, and implications for research. Journal of Service Research, 14(3), 252–271. https://doi. org/10.1177/1094670511411703
Hair, J. F., et al. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi. org/10.1177/002224378101800104https://doi. org/10.1177/002224378101800104
Bagozzi, R. P., & Yi, Y. (2012). Specification, eval uation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8–34. https://doi.org/10.1007/ s11747-011-0278-x
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant valid ity in variance-based structural equation model ing. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747 014-0403-8
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
Kumar, V., & Pansari, A. (2016). Competitive ad vantage through engagement. Journal of Marketing Research, 53(4), 497–514. https://doi.org/10.1509/ jmr.15.0044
Bagozzi, R. P., & Yi, Y. (2012). Specification, eval uation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8–34. https://doi.org/10.1007/ s11747-011-0278-x
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant valid ity in variance-based structural equation model ing. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747 014-0403-8
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
Kumar, V., & Pansari, A. (2016). Competitive ad vantage through engagement. Journal of Marketing Research, 53(4), 497–514. https://doi.org/10.1509/ jmr.15.0044
